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Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO 2 Flooding Using Artificial Intelligence Techniques

Author

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  • Amjed Hassan

    (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Salaheldin Elkatatny

    (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Abdulazeez Abdulraheem

    (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Carbon dioxide (CO 2 ) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO 2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO 2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO 2 -MMP). Actual field data were collected; 105 case studies of CO 2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO 2 -MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO 2 . An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO 2 -MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO 2 -MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO 2 -MMP. Ultimately, this work will improve the design of CO 2 flooding operations by providing a reliable value for the CO 2 -MMP.

Suggested Citation

  • Amjed Hassan & Salaheldin Elkatatny & Abdulazeez Abdulraheem, 2019. "Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO 2 Flooding Using Artificial Intelligence Techniques," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:7020-:d:295684
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    References listed on IDEAS

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    1. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Abdulwahab Ali & Tamer Moussa, 2019. "Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks," Energies, MDPI, vol. 12(11), pages 1-15, June.
    2. Haibat Ali & Jae-ho Choi, 2019. "A Review of Underground Pipeline Leakage and Sinkhole Monitoring Methods Based on Wireless Sensor Networking," Sustainability, MDPI, vol. 11(15), pages 1-24, July.
    3. Dhafer A. Al-Shehri, 2019. "Oil and Gas Wells: Enhanced Wellbore Casing Integrity Management through Corrosion Rate Prediction Using an Augmented Intelligent Approach," Sustainability, MDPI, vol. 11(3), pages 1-17, February.
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    Cited by:

    1. Tomislav Malvić & Josip Ivšinović & Josipa Velić & Jasenka Sremac & Uroš Barudžija, 2020. "Increasing Efficiency of Field Water Re-Injection during Water-Flooding in Mature Hydrocarbon Reservoirs: A Case Study from the Sava Depression, Northern Croatia," Sustainability, MDPI, vol. 12(3), pages 1-13, January.

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